Stochastic resource reallocation in two-stage production processes with undesirable outputs: An empirical study on the power industry

Alireza Amirteimoori, Reza Kazemi Matin and Amir Hossein Yadollahi

Socio-Economic Planning Sciences, 2024, vol. 93, issue C

Abstract: Due to the scarcity of fossil fuels in the future, the optimal use of these products can not only increase the efficiency of power plants, but it can also be effective in reducing the production of pollutants. To deal with these situations, optimal resource allocation and reallocation was studied using the data envelopment analysis (DEA) models. The current study adopted a resource allocation model in DEA framework when undesired outputs are produced in production process. This alternative resource allocation model is, however, sensitive to uncertainty of the data. In this contribution, we, therefore, introduce a stochastic resource allocation model when there are random data and undesirable products. An applied illustrative study to the power industry consisting 21 electricity production & distribution companies for eight years (2011–2019) is performed to compare the resource reallocations and their efficiencies. The important findings are: First, if we decide to deactivate two companies, the fuel consumption, employees and net electricity generation must be reduced. These reductions will lead to a reduction in pollutants. Second, the low price of electricity in Iran leads to excessive consumption of this product, which in turn leads to the inefficiency of many companies. In order to improve the performances of the companies, the amount of sold-out electricity must significantly be increased.

Keywords: Data envelopment analysis; Environmental pollution; Centralized resource reallocation; Two-stage processes; Stochastic data (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:soceps:v:93:y:2024:i:c:s0038012124000934

DOI: 10.1016/j.seps.2024.101894

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